Bibliographic record
Abstract
Welding of pyroclastic deposits involves flattening of glassy pyroclasts under a compactional load at temperatures above the glass transition temperature. Progressive welding is recorded by changes in the petrographic (e.g., fabric) and physical (e.g., density) properties of the deposits. Mapping the intensity of welding can be integral to studies of pyroclastic deposits, but making systematic comparisons between deposits can be problematical. Here we develop a scheme for ranking welding intensity in pyroclastic deposits on the basis of petrographic textural observations (e.g., oblateness of pumice lapilli and micro-fabric orientation) and measurements of physical properties, including density, porosity, point load strength and uniaxial compressive strength. Our dataset comprises measurements on 100 samples collected from a single cooling unit of the Bandelier Tuff and parallel measurements on 8 samples of more densely welded deposits. The proposed classification comprises six ranks of welding intensity ranging from unconsolidated (Rank I) to obsidian-like vitrophyre (Rank VI) and should allow for reproducible mapping of subtle variations in welding intensity between different deposits. The application of the ranking scheme is demonstrated by using published physical property data on welded pyroclastic deposits to map the total accumulated strain and to reconstruct their pre-welding thicknesses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".